import pandas as pd
import numpy as np

from datetime import datetime, timedelta

import holoviews as hv
import bokeh
from holoviews import opts
hv.extension('bokeh', 'matplotlib')

# считываем координаты регионов, список регионов и данные по фактическим и предсказанным данным
region_coords = pd.read_csv('region_coordinates.csv', sep=';').set_index('region')

regions = [int(reg) for reg in pd.read_csv('regions.csv').columns.values]

data = pd.read_csv('june_pred.csv', parse_dates=['tpep_pickup_datetime'])

data['pickup_day'] = data['tpep_pickup_datetime'].dt.floor('D')
data_grouped = data.groupby(['region', 'pickup_day'])[['trip_count', 'predicted']].sum().reset_index(['region', 'pickup_day'])
data_grouped.head(2)

	region	pickup_day	trip_count	predicted
0	1075	2016-05-31	26.0	38.877280
1	1075	2016-06-01	1533.0	1602.541519

# подготавливаем данные для визуализации
days = []
for day in range(1, 31):
    cells = []
    for reg in regions:
        south, north = list(region_coords[region_coords.index == reg][['south', 'north']].values[0])
        west, east = list(region_coords[region_coords.index == reg][['west','east']].values[0])
        cells.append({
            'region': reg,
            
            'trips': data_grouped[(data_grouped['region'] == reg) &
                                  (data_grouped['pickup_day'].dt.day == day)]['trip_count'].values[0],
            
            'pred': data_grouped[(data_grouped['region'] == reg) &
                                 (data_grouped['pickup_day'].dt.day == day)]['predicted'].values[0],
            
            'lats': [south, south, north, north],
            
            'lons': [west, east, east, west]
        })
    days.append(cells)

Фактическое и предсказанное количество поездок за июнь по дням.

trips_dic = {day: hv.Polygons(days[day-1], ['lons', 'lats'], [('trips', 'Trip count')]) for day in range(1,31)}
pred_dic = {day: hv.Polygons(days[day-1], ['lons', 'lats'], [('pred', 'Prediction')]) for day in range(1,31)}

holomap_preds = hv.HoloMap(pred_dic, kdims='Day')
holomap_trips = hv.HoloMap(trips_dic, kdims='Day')

layout = holomap_trips + holomap_preds
layout

Фактическое и предсказанное количество поездок за июнь по часам.

vdims = [('trip_count', 'Observed'), ('predicted', 'Predicted')]
ds = hv.Dataset(data, [('tpep_pickup_datetime', 'Time'), ('region', 'Area (Region)')], vdims)

layout = (ds.to(hv.Curve,'tpep_pickup_datetime', 'trip_count') + ds.to(hv.Curve, 'tpep_pickup_datetime', 'predicted')).cols(1)
layout.opts(
    opts.Curve(width=600, height=250, framewise=True))